{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "45ab8eee",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/miniconda3/envs/mindspore_py39/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "Building prefix dict from the default dictionary ...\n",
      "Loading model from cache /tmp/jieba.cache\n",
      "Loading model cost 0.489 seconds.\n",
      "Prefix dict has been built successfully.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'\n",
    "from mindnlp.peft import get_peft_config, get_peft_model, get_peft_model_state_dict, LoKrConfig, TaskType\n",
    "import mindspore\n",
    "import datasets\n",
    "from mindnlp.transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "from mindspore.dataset import NumpySlicesDataset, SequentialSampler\n",
    "from mindspore.dataset import text, GeneratorDataset, transforms\n",
    "\n",
    "model_name_or_path = \"bigscience/mt0-large\"\n",
    "tokenizer_name_or_path = \"bigscience/mt0-large\"\n",
    "\n",
    "text_column = \"sentence\"\n",
    "label_column = \"text_label\"\n",
    "max_length = 128\n",
    "lr = 1e-4\n",
    "num_epochs = 3\n",
    "batch_size = 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "80ceb99f",
   "metadata": {
    "id": "8d0850ac"
   },
   "outputs": [],
   "source": [
    "# creating model\n",
    "peft_config = LoKrConfig(task_type=TaskType.SEQ_2_SEQ_LM, target_modules=[\"q\", \"v\"],inference_mode=False,r=8,lora_alpha=32,rank_dropout=0.0,module_dropout=0.0,use_effective_conv2d=True,init_weights=True,)\n",
    "\n",
    "model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fa8b38c6",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "05978e96",
    "outputId": "ea9b7d40-010f-4df0-ec64-a7146a5f8b08"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainable params: 221,184 || all params: 1,229,802,496 || trainable%: 0.017985326970746365\n"
     ]
    }
   ],
   "source": [
    "model = get_peft_model(model, peft_config)\n",
    "model.print_trainable_parameters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ed242a05",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 140,
     "referenced_widgets": [
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      "f60e9915d2a74ca7bc010d7684f5acf6"
     ]
    },
    "id": "4ee2babf",
    "outputId": "3c413083-247d-47da-f25c-032764be0beb"
   },
   "outputs": [],
   "source": [
    "def process_dataset(source,   batch_size=32, shuffle=False):\n",
    "\n",
    "    column_names = ['input_ids', 'attention_mask','labels','text_labels']\n",
    "    \n",
    "    dataset = GeneratorDataset(source, column_names=column_names, shuffle=shuffle)\n",
    "    # transforms\n",
    "\n",
    "    dataset = dataset.batch(batch_size)\n",
    "\n",
    "    return dataset\n",
    "\n",
    "class MSDataset:\n",
    "    def __init__(self, filepath,tokenizer,max_length):\n",
    "        self.path = filepath\n",
    "        self.sentences = []\n",
    "        self.labels = []\n",
    "        self.text_labels = []\n",
    "        self._load()\n",
    "        self.tokenizer = tokenizer\n",
    "        self.max_length = max_length\n",
    "\n",
    "    def _load(self):\n",
    "        label_mapping = {\n",
    "            \"negative\": 0,\n",
    "            \"neutral\": 1,\n",
    "            \"positive\": 2\n",
    "        }\n",
    "        with open(self.path, encoding=\"iso-8859-1\") as f:\n",
    "            for line in f:\n",
    "                sentence, label_text = line.strip().split(\"@\")\n",
    "                self.sentences.append(sentence)\n",
    "                self.labels.append(label_mapping[label_text])\n",
    "                self.text_labels.append(label_text)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        sentence = self.sentences[index]\n",
    "        text_labels = self.text_labels[index]\n",
    "        model_inputs = self.tokenizer(sentence, max_length=self.max_length, padding=\"max_length\", truncation=True, return_tensors=\"np\")\n",
    "        labels = self.tokenizer(text_labels, max_length=3, padding=\"max_length\", truncation=True, return_tensors=\"np\")\n",
    "        labels = labels[\"input_ids\"]\n",
    "        labels[labels == self.tokenizer.pad_token_id] = -100\n",
    "        \n",
    "        return model_inputs['input_ids'], model_inputs['attention_mask'], labels,self.text_labels[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.sentences)\n",
    "    \n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)\n",
    "dataset = process_dataset(MSDataset(\"/root/test/FinancialPhraseBank-v1.0/Sentences_AllAgree.txt\",tokenizer,max_length),batch_size=batch_size)\n",
    "\n",
    "train_dataset, eval_dataset = dataset.split([0.9, 0.1])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f6de313d",
   "metadata": {
    "id": "f733a3c6"
   },
   "outputs": [],
   "source": [
    "# optimizer and lr scheduler\n",
    "optimizer = mindspore.nn.AdamWeightDecay(model.trainable_params(), learning_rate=lr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a3ca9831",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'input_ids': Tensor(shape=[6, 1, 128], dtype=Int64, value=\n",
      "[[[ 2894,   287,  5835 ...     0,     0,     0]],\n",
      " [[  259, 23846,   263 ...     0,     0,     0]],\n",
      " [[11055, 20672,   287 ...     0,     0,     0]],\n",
      " [[ 7468, 19934,  1532 ...     0,     0,     0]],\n",
      " [[ 7468, 19934,  1532 ...     0,     0,     0]],\n",
      " [[12781,  1503,   329 ...     0,     0,     0]]]), 'attention_mask': Tensor(shape=[6, 1, 128], dtype=Int64, value=\n",
      "[[[1, 1, 1 ... 0, 0, 0]],\n",
      " [[1, 1, 1 ... 0, 0, 0]],\n",
      " [[1, 1, 1 ... 0, 0, 0]],\n",
      " [[1, 1, 1 ... 0, 0, 0]],\n",
      " [[1, 1, 1 ... 0, 0, 0]],\n",
      " [[1, 1, 1 ... 0, 0, 0]]]), 'labels': Tensor(shape=[6, 1, 3], dtype=Int64, value=\n",
      "[[[59006,     1,  -100]],\n",
      " [[59006,     1,  -100]],\n",
      " [[59006,     1,  -100]],\n",
      " [[59006,     1,  -100]],\n",
      " [[59006,     1,  -100]],\n",
      " [[59006,     1,  -100]]]), 'text_labels': Tensor(shape=[6], dtype=String, value= ['neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral'])}\n"
     ]
    }
   ],
   "source": [
    "print(next(train_dataset.create_dict_iterator()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8024095a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6b3a4090",
    "outputId": "369cfce9-90f2-47a1-8653-ea1168943949"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 340/340 [09:28<00:00,  1.67s/it, train-loss=3.79]\n",
      "100%|██████████| 38/38 [00:20<00:00,  1.82it/s, eval-loss=33.95] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy=26.785714285714285 % on the evaluation dataset\n",
      "epoch=0: train_ppl=44.457796177776416 train_epoch_loss=3.7945403386564815 eval_ppl=2.443548598151249 eval_epoch_loss=0.8934513262764568\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 340/340 [09:42<00:00,  1.71s/it, train-loss=0.57]\n",
      "100%|██████████| 38/38 [00:19<00:00,  1.95it/s, eval-loss=5.08] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy=81.14035087719299 % on the evaluation dataset\n",
      "epoch=1: train_ppl=1.7650914597245262 train_epoch_loss=0.568202507583534 eval_ppl=1.1431496390290274 eval_epoch_loss=0.13378729402936396\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 340/340 [08:28<00:00,  1.49s/it, train-loss=0.31]\n",
      "100%|██████████| 38/38 [00:18<00:00,  2.02it/s, eval-loss=2.80] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy=87.71929824561403 % on the evaluation dataset\n",
      "epoch=2: train_ppl=1.367369146770264 train_epoch_loss=0.31288856281625 eval_ppl=1.0764534794629177 eval_epoch_loss=0.07367182226975415\n"
     ]
    }
   ],
   "source": [
    "# training and evaluation\n",
    "from mindspore import Tensor\n",
    "\n",
    "num_batches = len(train_dataset)\n",
    "num_batches_eval = len(eval_dataset)\n",
    "                       \n",
    "def forward_fn(input_ids,attention_mask,labels ):\n",
    "        output = model(\n",
    "            input_ids=input_ids,\n",
    "            attention_mask=attention_mask,\n",
    "            labels=labels,\n",
    "        )\n",
    "        return output.loss, output.logits\n",
    "    \n",
    "grad_fn = mindspore.value_and_grad(\n",
    "        forward_fn, None,optimizer.parameters, has_aux=True\n",
    "    )\n",
    "for epoch in range(num_epochs):\n",
    "    model.set_train(True)\n",
    "    total_loss, total_step = 0, 0\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    with tqdm(total=num_batches) as t:\n",
    "        for step, (input_ids,attention_mask,labels,_) in enumerate(train_dataset):\n",
    "            input_ids  = input_ids.squeeze(axis=1)\n",
    "            labels  = labels.squeeze(axis=1)\n",
    "            attention_mask = attention_mask.squeeze(axis=1)\n",
    "            (loss, logits), grad = grad_fn(input_ids,attention_mask,labels)\n",
    "            optimizer(grad)\n",
    "            total_loss += loss.asnumpy()\n",
    "            total_step += 1\n",
    "            curr_loss = total_loss / total_step\n",
    "            t.set_postfix({'train-loss': f'{curr_loss:.2f}'})\n",
    "            t.update(1)\n",
    "    model.set_train(False)\n",
    "    eval_loss = 0\n",
    "    total_step = 0\n",
    "    eval_preds = []\n",
    "    text_labels = []\n",
    "    with tqdm(total=num_batches_eval) as t:\n",
    "        for step, (input_ids,attention_mask,labels,text) in enumerate(eval_dataset):\n",
    "            input_ids = input_ids.squeeze(axis=1)\n",
    "            labels = labels.squeeze(axis=1)\n",
    "            attention_mask = attention_mask.squeeze(axis=1)\n",
    "            outputs = model(input_ids=input_ids,attention_mask=attention_mask,labels=labels)\n",
    "            loss = outputs.loss\n",
    "            eval_loss += loss.asnumpy()\n",
    "            total_step += 1           \n",
    "            eval_loss = total_loss / total_step\n",
    "            eval_preds.extend(\n",
    "                tokenizer.batch_decode(np.argmax(outputs.logits.asnumpy(), -1), skip_special_tokens=True)\n",
    "            )\n",
    "            text_str = str(text.asnumpy())\n",
    "            text_str = text_str.replace(\"[\", \"\").replace(\"]\", \"\").replace(\",\", \"\").replace(\"'\", \"\")\n",
    "            labels = text_str.split(\" \")\n",
    "            text_labels.extend(labels)\n",
    "            t.set_postfix({'eval-loss': f'{eval_loss:.2f}'})\n",
    "            t.update(1)\n",
    "    for pred, text_label in zip(eval_preds, text_labels):\n",
    "        if pred.strip() == text_label.strip():\n",
    "            correct += 1\n",
    "        total += 1\n",
    "    accuracy = correct / total * 100\n",
    "    print(f\"{accuracy=} % on the evaluation dataset\")\n",
    "    eval_epoch_loss = eval_loss / eval_dataset.get_dataset_size()\n",
    "    eval_ppl = np.exp(eval_epoch_loss)\n",
    "    train_epoch_loss = total_loss / train_dataset.get_dataset_size()\n",
    "    train_ppl = np.exp(train_epoch_loss)\n",
    "    print(f\"{epoch=}: {train_ppl=} {train_epoch_loss=} {eval_ppl=} {eval_epoch_loss=}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0988c170",
   "metadata": {
    "id": "a8de6005"
   },
   "outputs": [],
   "source": [
    "# saving model\n",
    "peft_model_id = f\"{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}\"\n",
    "model.save_pretrained(peft_model_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bd0fc4f3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bd20cd4c",
    "outputId": "0f25d837-80b1-476f-c897-92c3fef04fb2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
      "2468.52s - pydevd: Sending message related to process being replaced timed-out after 5 seconds\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "904K\tbigscience/mt0-large_LOKR_SEQ_2_SEQ_LM/adapter_model.ckpt\n"
     ]
    }
   ],
   "source": [
    "ckpt = f\"{peft_model_id}/adapter_model.ckpt\"\n",
    "!du -h $ckpt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8d075f95",
   "metadata": {
    "id": "76c2fc29"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[WARNING] ME(231218:140609833624064,MainProcess):2024-05-09-11:23:55.553.694 [mindspore/train/serialization.py:1378] For 'load_param_into_net', 558 parameters in the 'net' are not loaded, because they are not in the 'parameter_dict', please check whether the network structure is consistent when training and loading checkpoint.\n"
     ]
    }
   ],
   "source": [
    "from mindnlp.peft import PeftModel, PeftConfig\n",
    "import logging\n",
    "logging.getLogger('mindspore').setLevel(logging.ERROR)\n",
    "peft_model_id = f\"{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}\"\n",
    "\n",
    "config = PeftConfig.from_pretrained(peft_model_id)\n",
    "model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path)\n",
    "model = PeftModel.from_pretrained(model, peft_model_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "37d148cc",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "37d712ce",
    "outputId": "4828819a-b640-4f6c-91e3-878b648e9a75"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The share capital of Alma Media Corporation (business ID 1944757-4)is EUR 45,031,513.80 and it is divided into 75,052,523 shares .\n",
      "{'input_ids': Tensor(shape=[1, 35], dtype=Int64, value=\n",
      "[[ 486, 8364, 8646 ...  259,  260,    1]]), 'attention_mask': Tensor(shape=[1, 35], dtype=Int64, value=\n",
      "[[1, 1, 1 ... 1, 1, 1]])}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/我的mindnlp/mindnlp/mindnlp/transformers/generation/utils.py:1402: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation. Please use and modify the model generation configuration (see https://hf-mirror.com/docs/transformers/generation_strategies#default-text-generation-configuration )\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[    0 59006     1]]\n",
      "['neutral']\n"
     ]
    }
   ],
   "source": [
    "i = 13\n",
    "def load_dataset(filepath):\n",
    "    sentences = []\n",
    "    labels = []\n",
    "    with open(filepath, encoding=\"iso-8859-1\") as f:\n",
    "        for line in f:\n",
    "            sentence, label = line.strip().split(\"@\")\n",
    "            sentences.append(sentence)\n",
    "            labels.append(label)\n",
    "    \n",
    "    dataset = datasets.Dataset.from_dict({\n",
    "        \"sentence\": sentences,\n",
    "        \"label\": labels,\n",
    "    })\n",
    "    return dataset\n",
    "\n",
    "dataset = load_dataset(\"/root/test/FinancialPhraseBank-v1.0/Sentences_AllAgree.txt\")\n",
    "dataset = dataset.train_test_split(test_size=0.1)\n",
    "dataset[\"validation\"] = dataset[\"test\"]\n",
    "del dataset[\"test\"]\n",
    "\n",
    "inputs = tokenizer(dataset[\"validation\"][text_column][i], return_tensors=\"ms\")\n",
    "print(dataset[\"validation\"][text_column][i])\n",
    "print(inputs)\n",
    "\n",
    "outputs = model.generate(input_ids=inputs[\"input_ids\"], max_new_tokens=10)\n",
    "print(outputs)\n",
    "print(tokenizer.batch_decode(outputs ,skip_special_tokens=True))"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "gpuType": "T4",
   "machine_shape": "hm",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.19"
  },
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